A Privacy-Preserving Analysis Model of Human-to-Human Transmission of Infectious Diseases

doi:10.3969/j.issn.1671-1122.2023.03.004

Abstract

Abstract:

With the advent of the Internet of Everything and the era of big data, tracking the close contacts of patients through offline interactive data, and using health data to continuously detect the health status of close contacts bring new research perspectives for the analysis of human-to-human transmission of infectious diseases and provid a new way of blocking the spread of infectious diseases. However, such methods also have serious privacy leakage problems. Therefore, an Analysis Model of Human-to-Human Transmission of Infectious Diseases based on Offline Interaction and Health Data (AMHHTID-OIHD) was designed based on offline interaction and health data. The model consisted of six entities: trusted institutions, health cloud servers, interactive cloud servers, Centers for Disease Control (CDC), hospitals, and users. Finally, CDC found close contacts of the patient and classifies their health status in privacy-preserving way. Based on KNN classification and Gaussian Naive Bayes classification, combined with homomorphic encryption technology, the ciphertext conversion algorithm, privacy protection close contact search algorithm, and privacy protection health state classification algorithm of AMHHTID-OIHD were designed. The correctness and safety of the above algorithms were also analyzed and tested. The test results show that our model can complete the expected task objectives with a low overhead and privacy protection.

Key words: close contacts, infectious disease, privacy-preserving, offline interaction, health data

CLC Number:

TP309

LI Xiaohua, WANG Suhang, LI Kai, XU Jian. A Privacy-Preserving Analysis Model of Human-to-Human Transmission of Infectious Diseases[J]. Netinfo Security, 2023, 23(3): 35-44.

Figures/Tables 4

References 25

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